There is rarely any effort spent on context selection to help news caption generation in previous work. The LSTM-based method such as [1]} and [2]} coarsely encode the full article either as a general embedding vector or a set of sentence embedding vectors. Later, the transformer-based approach such as [3]} and [4]} directly encode the article at the token-level and achieved much better performance than the LSTM-based approach. However, they usually need to truncate the article to meet the encoding length limitation of the transformer architecture, which inevitably leads to the loss of useful information. [5]} is one of few works that addresses context selection for news image caption, where they train a cross-modal retrieval model VSE++ [6]} to find the sentences from the article that are most relevant to the image. However, the retrieved relevant sentences from VSE++ are limited to only capture visually grounded information. We argue that other valuable information, i.e. non-visual named entities, should be included as the relevant context to help news image caption generation.
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